
Data integrity issues: Data pipelines are often complex, and the format of the data may change over time. Fields may be renamed, categories may be added or split, and more. Such changes can have a major impact on your model's performance.

Data drift and concept drift: Data in the real world is constantly changing. This in turn may affect the distribution of the data that is being fed to the model, or that of the desired target prediction. Thus, the data the model was trained on becomes less and less relevant over time. The format of the data is still valid, but the model will become unstable and its predictive performance will deteriorate over time (in particular in the case of out-of-distribution [1]}).

